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Artificial Intelligence–Enabled De Novo Design of Novel Compounds that Are Synthesizable

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Artificial Intelligence in Drug Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2390))

Abstract

Development of computer-aided de novo design methods to discover novel compounds in a speedy manner to treat human diseases has been of interest to drug discovery scientists for the past three decades. In the beginning, the efforts were mostly concentrated to generate molecules that fit the active site of the target protein by sequential building of a molecule atom-by-atom and/or group-by-group while exploring all possible conformations to optimize binding interactions with the target protein. In recent years, deep learning approaches are applied to generate molecules that are iteratively optimized against a binding hypothesis (to optimize potency) and predictive models of drug-likeness (to optimize properties). Synthesizability of molecules generated by these de novo methods remains a challenge. This review will focus on the recent development of synthetic planning methods that are suitable for enhancing synthesizability of molecules designed by de novo methods.

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Author information

Authors and Affiliations

  1. Biotherapeutics and Medicinal Sciences, Biogen, Cambridge, MA, USA

    Govinda Bhisetti & Cheng Fang

Authors
  1. Govinda Bhisetti
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  2. Cheng Fang
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Corresponding author

Correspondence to Govinda Bhisetti .

Editor information

Editors and Affiliations

  1. Computational Drug Discovery, Evotec (UK) Ltd., Abingdon, Oxfordshire, UK

    Alexander Heifetz

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© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Bhisetti, G., Fang, C. (2022). Artificial Intelligence–Enabled De Novo Design of Novel Compounds that Are Synthesizable. In: Heifetz, A. (eds) Artificial Intelligence in Drug Design. Methods in Molecular Biology, vol 2390. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1787-8_17

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  • DOI: https://doi.org/10.1007/978-1-0716-1787-8_17

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1786-1

  • Online ISBN: 978-1-0716-1787-8

  • eBook Packages: Springer Protocols

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